Chest radiography is one of the most common imaging examinations. Chest radiography is used for screening, diagnosis, and management of many life-threatening diseases. Reading and diagnosing chest X-ray images may require careful observation and knowledge of anatomical principles, physiology, and pathology. Such factors increase the difficulty of developing a consistent and automated technique for reading chest X-ray images.
Advancements in machine learning-based systems and large datasets have enabled algorithms to match the performance of medical professionals in a wide variety of medical imaging tasks. However, automated diagnosis of chest X-rays is difficult as analysis requires an approach that can detect multiple pathologies simultaneously. Automated chest X-ray interpretation at the level of medical professionals could provide substantial benefits in many medical settings, from improved workflow prioritization and clinical decision support to large-scale screening and global population health initiatives. Only recently has the computational power and availability of large datasets allowed for the development of such an approach.
Developing the systems is challenging due to several factors including, but not limited to, high inter-rater variability in interpretation, high error rates in annotations due to the methods of annotation and inherent ambiguity in pathology appearance, limited data availability, lack of image quality, and site-specific image characteristics. The factors, in the context of machine learning-based systems, lead to overconfident systems with poor generalization on unseen data.